A Comparative Study of Differential Evolution Method and Hybridization Differential Evolution Method for Engineering Problems
Keywords:
Meta-Heuristics, Differential Evolution Algorithm, Unconstrained ProblemsAbstract
Nowadays, the meta-heuristic methods are powerful for various aspects including transportation and production planning. This study presents efficiency of Differential evolution (DE) and Hybridization differential evolution (HDE) for solving continuous unconstrained problems and machining problems. The results show that HDE is better than DE in terms of the mean, standard deviation and data distribution.
References
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2. Emad, E., Tarek, H. and Donald, G., 2005, “Comparison among Five Evolutionary-based Optimisation Algorithms,” Advanced Engineering Informatics, vol. 19, pp. 43-53.
3. Granville, V., Krivanek, M. and Rasson, J.P., 1994, “Simulated Annealing: a Proof of Convergence”, Pattern Analysis and Machine Intelligence, IEEE Transactions, vol. 16, Issue 6, pp. 652 – 656
4. Dorigo, M., Maniezzo V. and Colorni, A., 1996, “Ant System: Optimisation by a Colony of Cooperating Agents,” IEEE Transactions on Systems, Man, and Cybernetics Part B, vol. 26, numéro 1, pp. 29-41.
5. Clerc, M. and Kennedy, J., 2002, “The Particle Swarm-Explosion, Stability, and Convergence in a Multidimensional Complex Space”, IEEE Transactions on Evolutionary Computation, vol. 6, pp.58-73.
6. Storn, R., 1999, “System Design by Constraint Adaptation and Differential Evolution", IEEE Trans. on Evolutionary Computation, vol. 3, no. 1, pp. 22-34.
7. Kang Seok Lee and Zong Woo Geem, 2005, “A New Meta-Heuristic Algorithm for Continuous Engineering Optimisation: Harmony Search Theory and Practice”, Computer. Methods Apply. Mech. Eng. U 194, p. 3902–3933.
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Published
2019-07-07
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ResearchArticles
